DMSP and its APPLICATIONS

The Defense Meteorological Satellite Program (DMSP) is a series of weather
satellites developed to support Department of Defense (DoD) operations.
DMSP satellites are
in a near-polar sun-synchronous orbit at an altitude of approximately 835
kilometers. The orbital period is 101.4 minutes which produces 14.2 orbits
per day. Each DMSP satellite monitors the atmospheric, oceanographic, and
solar-geophysical environment of the earth.

The Operational Line Scan (OLS)
is the primary image acquisition system on DMSP spacecraft. Two spectral
channels are used in the OLS. One channel responds to reflected solar or
lunar radiation in the 0.4-1.1 micron range, chosen so as to provide maximum
contrast between earth, sea, and cloud elements of the image field, and
is termed the visible or L-band. Two different detectors are used for the
collection of L-band data; one collects fine resolution data (0.3 nautical
miles) and the other collects smooth resolution data (1.5 nautical miles).
The other channel of the OLS responds to emitted earth, atmosphere, and
cloud radiation in the 10.8-12.5 micron range (infrared). Data collection
at this channel is accomplished in the same manner as for L-band data. The
image data is collected at various sites and sent to Air Force Global Weather
Center (AFGWC) and the National
Geophysical Data Center (NGDC).
At AFGWC, a polar-stereographic map is projected onto the image and the
data is used in numerous ways. The IR data is 8-bit while the visible data
is 6-bit (64 shades). Because of recorder limitations aboard the satellites,
fine-resolution data is only available for pre-selected areas.

There are many additional environmental sensors on board DMSP satellites
but the main sensor that that will be discussed here is the Special Sensor
Microwave Imager (SSM/I).
The SSM/I is a seven channel, four frequency passive radiometer which detects
energy emitted by the earth's atmosphere in the microwave portion of the
spectrum. The frequencies utilized by the SSMI (selected to achieve specific
objectives for measuring parameters) are 19.35, 22.235, 37, and 85.5 GHz.
The data is downlinked to AFGWC, written to 8mm tapes, and sent to NGDC
for processing. At NGDC, the SSM/I pixels are geolocated using the satellite
ephemeris and satellite altitude corrections. The resultant imagery is 8-bit
at a resolution between 12.5 and 25 kilometers.

GIS APPLICATIONS OF DMSP IMAGERY

DMSP imagery has many applications aside from the purely meteorological
aspect (cloud detection). These applications range from biomass burning
detection (forest fires included here) to ice and snow detection.

OLS APPLICATIONS

City Lights: The OLS visible channel has the capability to detect
the lights of cities at night, allowing for a different perspective on the
spatial analysis of the urban landscape. Below is a sample of this type
of application. It is interesting to note that the lights of fishing vessels
were also detected in the Sea of Japan. Click here
to view more images of this application.

Lightning Detection: The OLS visible channel can also detect lightning
in thunderstorm clouds. Lightning is indicated by horizontal white lines
in the imagery. Click here
to see an example of this application.

Biomass Burning: The OLS sensor can be used to detect forest fires
as well as biomass burnings. The IR channel can be utilized when clouds
obscure the area of interest. Not only can the core area of a fire be detected,
but the smoke plume can be seen on the imagery as well, giving the analyst
an idea of which direction the smoke will travel and who could be affected
by the residual smoke. Click here
to view an image used to detect and estimate coverages of forest fires that
occurred in Idaho during August of 1994. Click here
to view images of various biomass burning events.

Snow and Ice Detection: The OLS sensor can detect snow and ice
fields using both the visible and infrared channels, although analysis is
more difficult in the IR mode. Snow is easily discernible from clouds -
it forms a dendritic pattern in mountains and, over flat terrain, rivers
and heat islands are visible. Ice is a little more difficult to discern
- best seen over clear areas or when the ice has large cracks or clearly
defined edges. This application can help in the determination of snow and
ice accumulations and its importance in the earth system. Click here to see an example
of snow in the Rocky Mountains, or here
to see ice in Hudson bay and Lake Winnipeg.

SSM/I APPLICATIONS

(Image of the SSM/I sensor)

The SSM/I sensor has many different types of applications, from atmospheric
phenomena to soil moisture analysis. Most GIS type applications of SSM/I
are accomplished at the 19 GHz frequency, which is sensitive to the characteristics
of the earth's surface. Due to the resolution of the sensor, these applications
are of a large-scale magnitude.

Bare Soil: The SSM/I can detect and classify bare soil, based
on reradiated heat from the earth's surface. Adding water to dry soil dramatically
changes its radiative characteristics and appearance in SSM/I imagery. Dampened
soil will have lower brightness temperatures than the same soil in a dry
condition. SSM/I can also estimate soil moisture, although this is subject
to a number of difficulties, ranging from the presence of vegetation to
the roughness of the soil. The image below shows a large portion of the
Middle East during winter in 4 SSM/I channels (clockwise from upper left
are 19, 22, 85, and 37 GHz). Note how well the waters of the Persian Gulf
contrast with the warmer land surrounding it except at 85GHz (85GHZ is used
mainly for cloud detection). The Tigris/Euphrates basin is also much cooler
than the adjoining desert, due to its increased soil moisture content. The
effect of different terrain elevations and soil types can be seen as well.
The linear feature in central Saudi Arabia is an elevated line of hills,
and is cooler because of both its altitude and soil condition.

Vegetation: The appearance of vegetation in SSM/I imagery is a
combination of modified radiation from the underlying soil and emissions
from the vegetation canopy itself. Below is an example of this type of SSM/I
imagery. The bright green area is lush rainforest in Brazil, Bolivia, and
elsewhere in the Amazon drainage basin, which appears very warm to the SSM/I.

Snow: The SSM/I can detect snow cover at frequencies above 20
GHz. It can also estimate the depth of snow cover from 0 mm to 400 mm, although
not always accurate under all snow conditions.

Sea Ice and Ice Concentration: SSM/I can detect and distinguish
sea ice from its surrounding waters. Ice has a higher emissivity than water,
and thus to the SSM/I looks significantly warmer than the surrounding water.
Using an Ice Concentration algorithm, the fraction of an ocean area that
is covered by ice can also be calculated from SSM/I data. This value is
given in percent, ranging from 0% to 100% with a quantization of 5%. Below
is an example of the end product of the SSM/I snow and ice function.

Surface Type: SSM/I data can be used to determine surface type
based on temperature. This complex algorithm can classify surface types
from glacial regions to desert regions. Below is an example of this application
in southeastern Canada. Some of the categories are: FL - Flooded Soil, WG
- Wet Ground, DS - Dry Snow, RG - Rain over Ground, etc.

Estimation of Ocean Surface Wind Speeds: As the wind blows across
open water, it roughens the surface and produces foam. Both of these effects
increase the emmisivity of the sea surface. Sea foam, in paricular, has
a very high emmisivity. The estimation of ocean surface wind speeds is a
complex algorithm that is normally calculated from a combination of SSM/I
channels in meters per second. It should be noted that other environmental
phenomena can degrade this estimation. Rain impaction on the sea surface
roughens the surface and causes higher brightness temperatures, which can
result in erroneous wind speeds. Also, large amounts of cloud water can
alter the wind signal. Below is an example of SSM/I derived ocean wind speeds.

MISCELLANEOUS

There are many new developments and techniques in the exploitation of
DMSP data. Air Force Global Weather Center (AFGWC) has developed multispectral
imagery from OLS DMSP fine data. The imagery is a colorized combination
of the visible and infrared channels. the visible channel is colored yellow
and the infrared channel is colored blue and then the two images are merged
together. Low clouds and snow which are bright in the visible channel but
not in the infrared channel result in yellow shades. High cirrus which is
bright in the infrared but not in the visible result in blue shades. This
multispectral imagery can be very beneficial. Relative cloud heights and
depths can be determined at a glance. Non-precipitating clouds can easily
be distinguished from more substantial cloudiness. When placed in a loop,
low-level and high-level winds can be deduced, aiding meteorological predictions
over regions that may not have conventional weather observations.

AFGWC also employs a solar elevation correction to some of its
DMSP data. Visible imagery appears darker in the morning and evening than
they do at noon. By calculating the geographic position of each point in
the image and knowing the time the image was captured, they can compute
how high in the sky the sun was. This algorithm then brightens the pixel
based on that sun angle.

DMSP OLS in the visible channel can also detect the aurora, also
referred to as the northern lights. This phenomena, believed to be of electrical
origin, is best seen in arctic regions. To see an example of this imagery,
click here.

NATIONAL SNOW AND ICE CENTER
(NSIDC): Established by NOAA in 1982 to serve as a national information
and referral center in support of glaciological reasearch. NSIDC archives
digital and analog snow and ice data, maintaining information about snow
cover and avalanches, glaciers and ice sheets, floating ice, ground ice
and permafrost, atmospheric ice, paleoglaciology and ice cores.

DMSP SYSTEM PROGRAM OFFICE
(SPO): The SPO is the Air Force agent responsible for the acquisition
and sustainment of the multi-service DSMP system of polar-orbiting weather
satellites.